Adaptive Energy Limiting for Transient Noise Suppression

ABSTRACT

The present disclosure describes aspects of adaptive energy limiting for transient noise suppression. In some aspects, an adaptive energy limiter sets a limiter ceiling for an audio signal to full scale and receives a portion of the audio signal. For the portion of the audio signal, the adaptive energy limiter determines a maximum amplitude and evaluates the portion with a neural network to provide a voice likelihood estimate. Based on the maximum amplitude and the voice likelihood estimate, the adaptive energy limiter determines that the portion of the audio signal includes noise. In response to determining that the portion of the audio signal includes noise, the adaptive energy limiter decreases the limiter ceiling and provides the limiter ceiling to a limiter module effective to limit an amount of energy of the audio signal. This may be effective to prevent audio signals from carrying full energy transient noise into conference audio.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S.Non-Provisional patent application Ser. No. 16/702,270, filed on Dec. 3,2019, which in turn claims priority to U.S. Provisional Application No.62/936,751, filed Nov. 18, 2019, the disclosures of which areincorporated by reference.

BACKGROUND

Audio conferences or video conferences often include many participants,with one or few of the participants actively speaking at any given time.When not speaking, the other participants typically produce noise, whichmay be picked up by their microphones and fed into the audio of theconference for all participants to hear. Example noises generated byconference participants may include typing on a keyboard, placing acoffee cup on a table, shuffling paper, moving chairs, shutting doors,and so on. Some of these noises have a transient characteristic that,unlike static or recurrent noise, prevent suppression throughconventional noise reduction techniques. Additionally, audio energy oftransient noise is typically as high, or higher, than energy levelsassociated with speech of the conference participants. As such, thesetransient noises are often fed into the conference audio as unsuppressedenergy, resulting in noise that may disrupt the speaker and listeners,overpower the speaker's voice, trigger residual echo suppression,falsely trigger audio or video switch schemes, or the like.

SUMMARY

This disclosure describes apparatuses and techniques of adaptive energylimiting for transient noise suppression. In some aspects, a method foradaptive energy limiting includes setting a limiter ceiling for an audiosignal to full scale and receiving a portion of the audio signal. Themethod then determines a maximum amplitude of the portion of the audiosignal and evaluates the portion of the audio signal with a neuralnetwork to provide a voice likelihood estimate for the portion of theaudio signal. Based on the maximum amplitude and the voice likelihoodestimate, the method determines that the portion of the audio signalincludes noise. In response to determining that the portion of the audiosignal includes noise, the method decreases the limiter ceiling. Thelimiter ceiling is then provided to a limiter module through which theaudio signal passes to limit an amount of energy of the audio signal. Byso doing, the audio signal may be prevented from carrying full energytransient noise into conference audio or subsequent audio processes,such as speaker selection for video conferencing.

In other aspects, an apparatus includes a network interface to receiveor transmit an audio signal over a data network and a limiter module tolimit energy of the audio signal. The apparatus also includes ahardware-based processor associated with the data interface and storagemedia storing processor-executable instructions for an adaptive energylimiter. The adaptive energy limiter is implemented to set a limiterceiling for the audio signal to full scale and provide, from the audiosignal, a frame of audio that corresponds to a duration of audio fromthe audio signal. The adaptive energy limiter then determines, for theframe of audio, a maximum amplitude of the audio signal and evaluatesthe frame of audio with a neural network to provide a voice likelihoodestimate for the frame of audio. Based on the maximum amplitude and thevoice likelihood estimate, the adaptive energy limiter determines thatthe frame of audio includes noise. The adaptive energy limiter thendecreases the limiter ceiling in response to the determination that theframe of audio includes noise and provides, to the limiter module, thelimiter ceiling to reduce the energy of the audio signal.

In yet other aspects, a system comprises a hardware-based processoroperably associated with an audio interface or a data interface by whichan audio signal is received and storage media storingprocessor-executable instructions for an adaptive energy limiter. Theadaptive energy limiter is implemented to set a limiter ceiling for theaudio signal to full scale and generate, based on the audio signal, aframe of audio that corresponds to a duration of audio from the audiosignal. The adaptive energy limiter then determines, for the frame ofaudio, a maximum amplitude of the audio signal and evaluates the frameof audio with a neural network to provide a voice likelihood estimatefor the frame of audio. Based on the maximum amplitude and the voicelikelihood estimate, the adaptive energy limiter determines that theframe of audio includes noise. The adaptive energy limiter thendecreases the limiter ceiling in response to the determination that theframe of audio includes noise and provides, to a limiter module, thelimiter ceiling to reduce the energy of the audio signal.

The details of one or more implementations of adaptive energy limitingfor transient noise suppression are set forth in the accompanyingdrawings and the following description. Other features and advantageswill be apparent from the description and drawings, and from the claims.This summary is provided to introduce subject matter that is furtherdescribed in the Detailed Description and Drawings. Accordingly, thissummary should not be considered to describe essential features nor usedto limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

This specification describes apparatuses and techniques of adaptiveenergy limiting for transient noise suppression with reference to thefollowing drawings. The same numbers are used throughout the drawings toreference like features and components:

FIG. 1 illustrates an example conferencing environment in which variousaspects of adaptive energy limiting for transient noise suppression canbe implemented.

FIG. 2 illustrates example device diagrams of a user device and aconference device that include respective instances of an adaptiveenergy limiter in accordance with one or more aspects.

FIG. 3 illustrates an example configuration of components that arecapable of implementing various aspects of adaptive energy limiting.

FIG. 4 illustrates an example method for adaptively limiting energy ofan audio signal in accordance with one or more aspects.

FIGS. 5A and 5B illustrate an example method for scaling an audio signalbased on an instantaneous voice likelihood provided by a neuralnetwork-enabled voice activity detector.

FIG. 6 illustrates example graphs in which energy of an audio signal islimiting in accordance with one or more aspects.

FIG. 7 illustrates a system diagram of components for implementingadaptive energy limiting for transient noise suppression in accordancewith one or more aspects.

DETAILED DESCRIPTION Overview

Audio conferences or video conferences often include many participants,with one or few of the participants actively speaking at any given time.When not speaking, the other participants typically produce noise, whichmay be picked up by their microphones and fed into the audio of theconference for all participants to hear. Example noises generated byconference participants may include typing on a keyboard, placing acoffee cup on a table, shuffling paper, moving chairs, shutting doors,and so on. Some of these noises have a transient characteristic that,unlike static or recurrent noise, prevent suppression throughconventional noise reduction techniques. Additionally, audio energy oftransient noise is typically as high, or higher, than energy levelsassociated with speech of the conference participants. As such, thesetransient noises are often fed into the conference audio as rawunsuppressed energy, resulting in noise that may disrupt the speaker andlisteners, overpower the speaker's voice, trigger residual echosuppression, falsely trigger audio or video switch schemes, or the like.

Because conventional techniques of noise reduction are unable tomitigate transient noise, there are multiple negative consequences thataffect conference call participants. Generally, the unsuppressed noiseis let through to the other end of the call, disturbing both the speakerand other listeners. This unsuppressed noise may also, when let throughto a current speaker in the call, trigger residual echo suppression thatdampens the speaker's voice or affect backend speaker selection schemessuch as top-3 filtering (e.g., passing through respective audio of thethree call participants with the most energy). Additionally, theconference system may incorrectly prioritize noisy participants overactively speaking participants or interrupt video switching schemes byswitching a video feed of the speaker to the participant generating thenoise.

Some conventional techniques involve having participants that are notcurrently speaking manually mute their respective microphone. Mutingsolutions, however, are undesirable and inconvenient because thesesolutions result in unnatural conversational flow and often cause issueswhen participants forget to unmute their microphone before speaking.Manually muting microphones can be especially frustrating in a largemeeting room in which many participants take turns speaking such thatmuting occurs very frequently. For example, anytime someone wants tospeak to the other participants, that person would need to reach for aremote control or button on a device to unmute their microphone, andthen remember to mute again afterwards. As such, manual muting thatrelies on timely manual interaction from all participants isinconvenient and often ineffective at suppressing transient noise.

Other conventional techniques also typically fail at preventingtransient noise from entering the conference audio or do so at the costof other impairments to call flow or quality. For example, some phonesinclude noise gates that auto-mute unless there is strong energy presentin an audio stream. These noise gates, however, lead to choppy qualityaudio and often let high-energy noise through to the conference audio.Other noise reduction techniques only work for stationary or slightlynon-stationary noise (e.g., fans, traffic, background babble), nottransient noise, which is sudden, non-constant, and high energy. Inother cases, keyboard suppression predicts when keyboard sounds willoccur and selectively suppresses these sounds. This suppression islimited to cases where the typing happens on the same laptop that ishosting the meeting, and only works for keyboard noise. Accordingly,conventional noise suppression techniques for conferences calls areunable to suppress or limit transient noise, which often interferes withcall flow and quality.

This document describes apparatuses and techniques for adaptive energylimiting for transient noise suppression. As described, participants ofa conference call may generate transient noise that, when allowed intothe conferenced audio, often disrupts the speaker and otherparticipants. Transient noise may also interfere with or degradeconference service processes for audio and video features, such as audiostream or video stream selection (e.g., active speaker) for presentationto other participants. Generally, aspects of adaptive energy limitingmanage or control a maximum level of energy that a participant isallowed to contribute based on the participant's recent history ofproducing noise or speech. In various aspects, an adaptive energylimiter of a user device or conference system sets a limiter ceiling foran audio signal to full scale and receives a portion of the audiosignal. For the portion of the audio signal, the adaptive energy limiterdetermines a maximum amplitude and evaluates the portion with a neuralnetwork to provide a voice likelihood estimate. Based on the maximumamplitude and the voice likelihood estimate, the adaptive energy limiterdetermines that the portion of the audio signal includes noise. Inresponse to determining that the portion of the audio signal includesnoise, the adaptive energy limiter decreases the limiter ceiling andprovides the limiter ceiling to a limiter module effective to limit anamount of energy of the audio signal. By so doing, the adaptive energylimiter may prevent the audio signal from carrying full energy transientnoise into conference audio or subsequent audio processes, such asspeaker selection for video conferencing.

By way of example, if a participant makes noise, the ceiling of energylet through by the adaptive energy limiter will gradually be decreased.Generally, this will result in future sudden noise generated by thatparticipant being less intrusive, and more easily ignored by otherconference service algorithms, such as speaker selection for videoconferencing. In some aspects, the ceiling of audio energy decreases toa minimum level after approximately 10 to 15 seconds of medium orhigh-energy noise, after which audio energy (e.g., noise energy) fromthat participant will be very limited. When that participant does startto speak, the adaptive energy limiter may reset the ceiling of audioenergy to a maximum level (e.g., speech level or full scale), to letspeech audio of the participant through to the other conferenceparticipants. The adaptive energy limiter does so quickly, such that thetransient noise suppression provided by the adaptive energy limiter haslittle detrimental effect to the speech audio of the conference call.Alternately or additionally, if a participant is silent, quiet, ormaking low energy background noise, the adaptive energy limiter maymaintain or leave the ceiling of audio energy high, as to not effectspeech audio when the participant begins to speak.

Generally, aspects of adaptive energy limiting for transient noisesuppression limit energy of transient noise without impairing quality ofspeech audio of a conference call or voice call. For example, by usinglong-term statistical properties of noise and/or speech in the contextof audio or video conferencing scenarios, the adaptive energy limitermay substantially reduce an amount or effects of transient noise whileminimally affecting speech. In other words, the adaptive noise limiterdoes not attempt to remove noise from concurrent noise and speech, whichis otherwise a typical issue with conventional noise reductiontechniques when trying to remove noise, particularly noise that may beconfused with speech.

In various aspects of adaptive energy limiting, amplitude of an audiosignal is measured for a time, and together with the other describedutilizations of statistical properties, a limiter ceiling for audioenergy is configured to prevent or suppress transient noise fromentering a conference call. In some cases, a neural network isimplemented to provide statistical properties about the audio signal. Inaccordance with various aspects, a small neural network has sufficientaccuracy for such a task, such that no special acceleration hardware isneeded, and speech quality does not suffer by the limits in accuracy ofthe neural network or associated voice activity detector (VAD).Alternately or additionally, an adaptive energy manager may beimplemented to adjust or manage gain or sub-band gain of an audio signalbased on the audio signal evaluations described herein.

As such, various aspects of energy limiting (or energy management) maybe implemented to limit or reduce an amount of energy an audio signal isable to carry into a conference service, through a conference call, orout to conference call participants. In other words, for eachparticipant, an adaptive energy limiter may track a noise debt thatbuilds up as a participant continues to make noise. As the noise debtbuilds (or energy limit decreases), the adaptive energy limiter preventsor disallows that participant from sending a lot of energy into a calluntil that participant proves that they're sending speech (e.g., bysending a statistically significant amount of speech audio). Theadaptive energy limiter may also effectively suppress transient noise byusing (e.g., via the neural network) statistical energy differences oftransient noises (e.g., high energy), vowels (e.g., medium energy) andconsonants (e.g., low energy) to allow speech (e.g., consonants) to passthrough perceptually unaffected even when transient noises are reduced20 dB or more. Aspects of adaptive energy limiting may achieve such aneffect through use of the limiter ceiling of audio signal energy and/orthrough management of sub-band gains used to process the audio signalsof participants.

While any number of different environments, systems, devices, and/orvarious configurations can implement features and concepts of thedescribed techniques and apparatuses for adaptive energy limiting fortransient noise suppression, aspects of adaptive energy limiting fortransient noise suppression are described in the context of thefollowing example environment, devices, configuration, methods, andsystem.

Example Environments

FIG. 1 illustrates an example environment 100 in which various aspectsof adaptive energy limiting for transient noise suppression can beimplemented. In the example environment 100, user devices 102 maycommunicate audio and/or video through a conference system 104 in whichaccess to the system is provided by a conference service 106 (e.g.,cloud-based meeting or conference service). User devices 102 in thisexample include a smartphone 102-1, laptop computer 102-2, tabletcomputer 102-3, smartwatch 102-4, telephone 102-5, conference bridge102-6, and video conference display 102-7. Although illustrated asdevices, a user device may be implemented as any suitable computing orelectronic device, such as a mobile communication device, a computingdevice, a client device, an entertainment device, a gaming device, amobile gaming console, a personal media device, a media playback device,a charging station, an Advanced Driver Assistance System (ADAS), apoint-of-sale (POS) transaction system, a health monitoring device, adrone, a camera, a wearable smart-device, a navigation device, amobile-internet device (MID), an Internet home appliance capable ofwireless Internet access and browsing, an Internet-of-Things (IoT)device, a Fifth Generation New Radio (5G NR) user equipment, and/orother types of user devices.

Generally, a respective user of a user device 102 may interact withother users through audio and/or video data exchanged through a data orvoice connection to the conference service 106. In some aspects, eachuser device 102 participating in an instance of a conference callfacilitated by the conference service 106 provides an audio signal 108and/or video signal through a respective connection with the conferenceservice. For example, any or all of the user devices 102 may provide achannel of audio signals 108 (or audio data) that corresponds to audiocaptured by a microphone of that device. During a conference call,participants typically take turns speaking while other inactive ornon-speaking participants listen or watch. Some of the participants,however, may choose to move a chair, write an e-mail, or take notes on acomputer. Such moving and typing activities may generate transientnoise, which may include a sound or sound wave with a short, pulse-likesignal characteristic. Other potential sources of transient noise mayinclude clicking noise from a computer mouse, moving items on a table orwork surface, doors closing, phone keypad or ring tones, or the like.For example, if two participants, each at a respective endpoint of aconference or voice call are situated proximate each other in anopen-plan office, one of the participants using a smartphone 102-1 andthe other using a laptop computer 102-2, potential transient noise maybe generated at both endpoints when the participant using the laptopcomputer 102-2 starts typing.

In aspects of adaptive energy limiting for transient noise suppression,the conference service 106 includes an instance of an adaptive energylimiter 110 (adaptive limiter 110), which may limit or manage energy ofan audio signal to suppress various forms of transient noise. Althoughillustrated with reference to the conference service 106, any or all ofthe user devices 102 may also include an instance of the adaptive energylimiter 110. Thus, an adaptive energy limiter 110 may limit or manageenergy of an audio signal sent to the conference service 106, processedby the conference service 106, or sent by the conference service toother user devices 102. The adaptive energy limiter 110 is associatedwith or has access to a neural network 112, which may be implemented asa recurrent neural network (RNN). In this example, the neural network112 includes a voice activity detector 114 (VAD 114) that may beconfigured to provide indications of voice likelihood for audio signalsor frames of audio. For example, the adaptive energy limiter 110 may usethe voice activity detector 114 to obtain an indication of voicelikelihood for a frame of audio. Such an indication may be useful todetermine whether the audio signal or frame of audio is more likelyspeech or noise. Alternately or additionally, the voice activitydetector 114 can be implemented as a neural network-enabled voiceactivity detector that uses a neural network to determine or provide avoice likelihood measurement for a sample of audio signal or audioframe.

FIG. 2 illustrates at 200 example device diagrams of a user device 102and a conference device 202, which may provide the conference service106. Although each device is shown with an instance of an adaptiveenergy limiter, aspects of adaptive energy limiting may be implementedon one device, both devices, or in coordination between devices. Forexample, an adaptive energy limiter 110 of a user device 102 mayinteract with adaptive energy limiter 110 or neural network 112 of theconference device 202 to set a limiter ceiling value at the user device102. Shown in exemplary configurations, the user device 102 or theconference device 202 may also include additional functions, components,or interfaces omitted from FIG. 2 for the sake of clarity or visualbrevity. Alternately or additionally, any respective components of theuser device 102 or the conference device 202 may be implemented in wholeor part as hardware logic or circuitry integrated with or separate fromother components.

In this example, the user device 102 includes network interfaces 204 forexchanging data, such as audio signals or video streams, over varioustypes of networks or communication protocols. Generally, the networkinterfaces 204 can be implemented as any one or more of a serial and/orparallel interface, a wireless interface, a wired interface, or a modemfor transmitting or receiving data or signals. In some cases, thenetwork interfaces 204 provide a connection and/or communication linkbetween the user device 102 and a communication network by which otheruser devices 102, and the conference device 202, communicate audiosignals 108, video data, or the like for conferenced mediacommunication. The user device 102 also includes at least one microphone206 to capture audio (e.g., speech, sound, or noise) from an environmentof the user device 102 and at least one speaker 208 to generate audio orsound based on audio data of the user device 102. In some aspects, themicrophone captures audio generated by a user, such as speech, andprovides an audio signal to audio circuitry (not shown) of the userdevice 102 for encoding or other signal-processing.

The user device 102 also includes processor(s) 210 and computer-readablestorage media 212 (CRM 212). The processor(s) 210 may be a single coreprocessor or a multiple core processor composed of a variety ofmaterials, such as silicon, polysilicon, high-K dielectric, or the like.The computer-readable storage media 212 is configured as storage, andthus does not include transitory signals or carrier waves. The CRM 212may include any suitable memory or storage device such as random-accessmemory (RAM), static RAM (SRAM), dynamic RAM (DRAM), non-volatile RAM(NVRAM), read-only memory (ROM), or Flash memory useable to store devicedata 214 of the user device 102.

The device data 214 may include user data, multimedia data (e.g., audiodata or video data), applications 216 (e.g., media conference clientapplication), user interface(s) 218, and/or an operating system of theuser device 102, which are accessible to or executable by processor(s)210 to enable audio or video conferencing and/or other user interactionwith the user device 102. The user interface 218 can be configured toreceive inputs from a user of the user device 102, such as to receiveinput from a user that may define and/or facilitate one or more aspectsof adaptive energy limiting for transient noise suppression. The userinterface 218 can include a graphical user interface (GUI) that receivesthe input information via a touch input. In other instances, the userinterface 218 includes an intelligent assistant that receives the inputinformation via an audible input. Alternately or additionally, theoperating system of the user device 102 may be maintained as firmware oran application on the CRM 212 and executed by the processor(s) 210.

The CRM 212 also includes an adaptive energy limiter 110, neural network112, and voice activity detector 114. In various aspects, the adaptiveenergy limiter 110 utilizes the neural network 112 and/or voice activitydetector 114 (VAD 114) to determine whether an audio signal comprisesspeech or noise. Based on this determination, the adaptive energylimiter 110 may decrease a limiter ceiling to limit energy of noise thatwould otherwise disrupt a conference call or voice call if allowed topass through at full energy. The implementations and uses of theadaptive energy limiter 110, neural network 112, and/or voice activitydetector 114 vary and are described throughout the disclosure.

Aspects and functionalities of the user device 102 may be managed viaoperating system controls presented through at least one applicationprogramming interface 220 (API 220). In some aspects, the adaptiveenergy limiter 110 or an application of the user device 102 accesses anAPI 220 or an API service of the user device 102 to control aspects andfunctionalities of audio or video conference applications. For example,the adaptive energy limiter 110 may access low-level audio processorsettings of the user device 102 to implement aspects of adaptive energylimiting, such as to set a minimum limiter ceiling level, adjust audiogain setting, manage respective signal levels of incoming and outgoingaudio signals, or the like. The CRM 212 of the user device 102 may alsoinclude a user device manager 222, which can be implemented in whole orpart as hardware logic or circuitry integrated with or separate fromother components of the user device 102. In at least some aspects, theuser device manager 222 configures the microphone 206 and other audiocircuitry of the user device 102 to implement the techniques fortransient noise suppression as described herein.

The user device 102 also includes a display 224 for displaying and/orproviding information or a video feed to a user. For example, throughthe display 224, the user device 102 may provide the user with a videofeed from a video conference enabled by the conference service 106.Alternately or additionally, the user device 102 may also include acamera (not shown) to enable generation of a video feed from the userdevice 102 for multimedia conferencing.

The conference device 202 may be implemented as a computing device,server, cloud-based hardware, or other resources through which theconference service 106 is provided to the user devices 102. Generally,the conference device 202 may serve as a collector and/or arbiter ofmultimedia data or streams for an instance of a conference call. Assuch, the conference device 202 may implement aspects of adaptive energylimiting with respect to inbound audio data received from user devices102, internal multimedia processing operations, or outbound audio datatransmitted to the user devices 102 as part of a conference or voicecall.

In this example, the conference device 202 includes network interfaces226 for exchanging data, such as audio signals or video streams, overvarious types of networks or communication protocols. Generally, thenetwork interfaces 226 can be implemented as any one or more of a serialand/or parallel interface, a wireless interface, a wired interface, or amodem for transmitting or receiving data or signals. In some cases, thenetwork interfaces 226 provide a connection and/or communication linkbetween the conference device 202 and a communication network by whichthe user devices 102 communicate audio signals 108, video data, or thelike to for conferenced media communication.

In this example, the conference device 202 also includes processor(s)228, or compute resources, and computer-readable storage media 230 (CRM230). The computer-readable storage media 230 is configured as storage,and thus does not include transitory signals or carrier waves. The CRM230 may include any suitable memory or storage device such as RAM, SRAM,DRAM, NVRAM, ROM, or Flash memory useable to store multimedia data 232of the conference device 202.

The multimedia data 232 of the conference device 202 may include audiodata, audio signals, or video data useful to facilitate conference callsthrough an instance of the conference service 106. The multimedia data232 and conference service 106, as well as other applications (e.g.,media conference server applications) and/or an operating system of theconference device 202 may be accessible to or executable by processor(s)228 to enable audio or video conferencing for multiple user devices 102.

In this example, the CRM 230 also includes an instance of the adaptiveenergy limiter 110, neural network 112, and voice activity detector 114.As noted, aspects of adaptive energy limiting may be implemented by auser device 102, conference device 202, or a combination of bothdevices. In various aspects, the adaptive energy limiter 110 utilizesthe neural network 112 and/or voice activity detector 114 to determinewhether one or more audio signals comprise speech or noise. Based onthis determination, the adaptive energy limiter 110 of the conferencedevice 202 may decrease a limiter ceiling for a respective audio signalor audio feed to limit energy of noise that would otherwise disrupt aconference call or voice call if allowed to pass through at full energy.The implementations and uses of the adaptive energy limiter 110, neuralnetwork 112, and/or voice activity detector 114 vary and are describedthroughout the disclosure.

Aspects and functionalities of the conference device 202 may be managedvia system controls presented through at least one applicationprogramming interface (API) of an API library 234. In some aspects, theadaptive energy limiter 110 or an application of the conference device202 accesses an API or library of the API library 234 to implementaspects of transient noise limiting. For example, the adaptive energylimiter 110 may be implemented as part of or in conjunction with aweb-based real-time communications library.

FIG. 3 illustrates at 300 an example configuration of components thatare capable of implementing various aspects of adaptive energy limiting.Generally, the components of FIG. 3 may be embodied on a user device102, a conference device 202, or a combination thereof. In some aspects,the components shown at 300 are implemented as an integrated component(e.g., system-on-chip) of one device and/or in combination with a memorystoring processor-executable instructions to provide respectivefunctionalities of one or more components. As such, the configuration ofcomponents shown in FIG. 3 is non-limiting and may be implemented on anysuitable device, combination of devices, and/or as hardware (e.g., logiccircuitry) combined with firmware or software to provide the describedfunctionalities.

In some aspects, an audio signal 108 is sliced or partitioned into audioframes 302 that correspond to respective portions of the audio signal.For example, each of the audio frames 302 may correspond to a portion,segment, or duration of audio (e.g., speech and/or noise) of the audiosignal 108. In some cases, an audio frame 302 or frame of audiocorresponds to a range of approximately five milliseconds to 50milliseconds of audio (e.g., 10 millisecond of audio). Alternately oradditionally, the audio frames 302 may be converted from a time domainto a frequency domain, such as to enable spectral analysis or otherfrequency domain-based processing.

As shown in FIG. 3, the example components include an amplitude detector304 and a neural network 112, which includes or provides a voiceactivity detector 114 for processing the audio frames 302. Generally,the amplitude detector 304 measures or determines an amplitude of theaudio signal 108 that corresponds to an audio frame. For example, theamplitude detector 304 may generate or provide an indication of amaximum amplitude 306 for a frame of audio or portion of audio signal.In some aspects, the adaptive energy limiter 110 determines or updatesan average amplitude 308 (e.g., moving average) for the audio signal 108or audio frames 302 based on multiple maximum amplitudes 306.

The neural network 112 may be implemented as a network that operates ona processor of a user device 102 to provide voice likelihood estimatesfor the audio frames 302. Alternately or additionally, the neuralnetwork 112 may be implemented as a recurrent neural network (RNN) ormachine-learned model with a memory (e.g., RNNoise). In some aspects,the voice activity detector 114 provides, for one or more of the audioframes, an instantaneous voice likelihood 310 (IVL 310). Althoughdescribed as a neural network-enabled voice activity detector, othertypes of voice activity detection or voice classification may be used.

For example, the neural network 112 and/or voice activity detector 114may be implemented as a neural network (e.g., deep neural network (DNN))comprising an input layer, an output layer, and one or more hiddenintermediate layers positioned between the input layer and the outputlayer of the neural network. Any or all nodes of the neural network maybe in turn fully connected or partially connected between the layers ofthe neural network. A voice activity detector 114 may be implementedwith or through any type of neural network, such as a convolutionalneural network (CNN) including GoogleNet or similar convolutionalnetworks. Alternately or additionally, a voice activity detector 114 ormachine-learned voice activity detection model may include any suitablerecurrent neural network (RNN) or any variation thereof. Generally, theneural network 112 and/or voice activity detector 114 employed by theadaptive energy limiter may also include any other supervised learning,unsupervised learning, reinforcement learning algorithm, or the like.

In various aspects, a neural network 112 and/or voice activity detector114 associated with the adaptive energy limiter 110 may be implementedas a recurrent neural network (RNN) with connections between nodes thatform a cycle to retain information from a previous portion of an inputdata sequence for a subsequent portion of the input data sequence (e.g.,previous audio frames of speech or noise generated by a participant). Inother cases, a neural network 112 is implemented as a feed-forwardneural network having connections between the nodes that do not form acycle between input data sequences. Alternately, a neural network 112may be implemented as a convolutional neural network (CNN) withmultilayer perceptrons where each neuron in a particular layer isconnected with all neurons of an adjacent layer. In various aspects ofadaptive energy limiting, the neural network 112 and/or voice activitydetector 114 may use previous determinations of noise or speech by aparticipant to predict or determine whether subsequent frames of anaudio signal include speech or noise that may be suppressed.

Generally, the neural network 112 may enable the determination of voicelikelihood estimations that quickly converge to high statisticalconfidence, particularly in the presence of vowel sounds. By way ofreview, transient noise often has more full-band energy than vowels, andeven more so than consonants in speech. Thus, in utilizing a statisticalconfidence provided by the neural network 112, the adaptive energylimiter is able to leverage historical noise or speech patterns of aparticipant to distinguish between noise, vowels, and consonants ofspeech. In other words, speech and noise tend to come in bursts, thatis, a participant that has recently spoken is more likely to continuespeaking in the near future (e.g., sub-second). Alternately, aparticipant that produced noise in the recent past is more likely togenerate additional noise in the future. In some cases, any lagintroduced by the adaptive energy limiter is imperceptible to conferencecall participants, yet the neural network 112 is able to more accuratelydetermine in retrospect (e.g., a few 100 milliseconds) whether audio ofthe frame or signal is noise or speech, than to such in real-time.

Based on one or more of the instantaneous voice likelihoods 310, theadaptive energy limiter 110 may determine an aggregate speech likelihoodestimate 312 (ASLE 312) for the audio signal 108 or audio 302. Theaggregate speech likelihood estimate 312 may be configured or updatedbased on a current aggregate speech likelihood estimate 312 and/or athreshold for detection of voice or noise. For example, in some cases,the adaptive energy limiter 110 increases the aggregate speechlikelihood estimate 312 in response to an instantaneous voice likelihood310 exceeding the current aggregate speech likelihood estimate 312, aswell as exceeding a threshold for voice detection. In other cases, theadaptive energy limiter 110 may decrease the aggregate speech likelihoodestimate 312 in response to an instantaneous voice likelihood 310 notexceeding the current aggregate speech likelihood estimate 312 or notexceeding a threshold for voice detection.

The adaptive energy limiter 110 also includes or provides a limiterceiling 314 by which energy of the audio signal 108 may be limited, suchas to suppress energy of transient noise. Generally, the limiter ceiling314 is provided to an audio signal limiter module 316 through which theaudio signal 108 passes before transmission to other audio components orprocesses. The audio signal limiter module 316 may pass audio signalthrough at full scale (e.g., unreduced or not limited) or a reducedscale or reduced amplitude as specified by the limiter ceiling 314 setby the adaptive energy limiter 110. In the context of FIG. 3, based onthe limiter ceiling 314 provided by the adaptive energy limiter 110, theaudio signal limiter module 316 limits or decreases energy of the audiosignal 108 to provide or generate an energy-limited audio signal 318. Invarious aspects, the adaptive energy limiter 110 limits the energy of anaudio signal determined to be, or include, noise in order to suppressthe noise and likely future noise. The energy-limited audio signal 318may then be transmitted to audio-based processing 320 for subsequentprocessing or use for other features (e.g., speaker selection), beforebeing included in conference audio 322, which is shared with otherparticipants of an audio or video conference call.

Example Methods

Example methods 400 and 500 are described with reference to FIG. 4, FIG.5A, and FIG. 5B in accordance with one or more aspects of adaptiveenergy limiting for transient noise suppression. Generally, methods 400and 500 illustrate sets of operations (or acts) performed in, but notnecessarily limited to, the order or combinations in which theoperations are shown herein. Further, any of one or more of theoperations may be repeated, combined, reorganized, skipped, or linked toprovide a wide array of additional and/or alternate methods. In portionsof the following discussion, reference may be made to example conferenceenvironment 100 of FIG. 1, example devices of FIG. 2, example componentsof FIG. 4, example systems of FIG. 7, and/or entities detailed in FIG.1, reference to which is made for example only. The techniques andapparatuses described in this disclosure are not limited to embodimentor performance by one entity or multiple entities operating on onedevice.

Method 400 is a method performed by a user device 102 or conferencedevice 202. The method 400 limits an amount of energy of an audio signalto mitigate effects associated with transient noise in conferenceenvironments or other audio processes (e.g., speaker selection for videoconferencing). In some aspects, operations of the method 400 areimplemented by or with an adaptive limiter 110, neural network 112,and/or voice activity detector 114 of the user device 102 or conferencedevice 202.

At 402, a limiter ceiling for an audio signal is set to full scale. Insome cases, the limiter ceiling or limiting value is set to full scaleon initialization of the adaptive energy limiter or in response tospeech by a participant for which an audio signal is being processed fornoise suppression.

At 404, a portion of the audio signal is received. The portion of theaudio signal may include a frame of audio, audio frame, segment of theaudio signal, or the like. In some cases, the audio signal is receivedand separated into frames of audio for analysis by the adaptive energylimiter. For example, a frame of the audio may correspond to a range ofapproximately five milliseconds to 50 milliseconds of audio. Alternatelyor additionally, the frame of audio can be converted from a time domainto a frequency domain to enable spectral analysis or other frequencydomain-based processing.

At 406, a maximum amplitude of the portion of the audio signal isdetermined. The maximum amplitude may be determined for the portion ofaudio signal that corresponds to a frame of audio or a duration of audio(e.g., 10 milliseconds). In some cases, the maximum amplitude of theaudio signal is compared to a threshold to determine if a participant issilent, quiet, or otherwise not generating noise. Optionally, fromoperation 406, the method 400 may return to operation 404 if the audiosignal is quiet or silent. By so doing, energy of the silentparticipant's speech will not be reduced if and when the participantbegins to speak.

At 408, the portion of the audio signal is evaluated with a neuralnetwork to provide a voice likelihood estimate. In some aspects, theportion of the audio signal or a frame of audio is evaluated with theneural network or a neural network-enabled voice activity detector toprovide an instantaneous voice likelihood for the portion of the audiosignal or the audio frame. Generally, the instantaneous voice likelihoodmay indicate if the audio stream is more likely speech or more likelynoise, which the adaptive energy limiter would suppress.

At 410, a determination is made, based on the maximum amplitude and thevoice likelihood estimate, as to whether the portion of the audio signalincludes speech or noise. For example, if the maximum amplitude of theportion of the audio signal exceeds a moving average of the maximumamplitude (e.g., maximum average plus a small modifier) and theinstantaneous voice likelihood is less than 0.5 or 50% (indicatingnoise), it may be determined that the portion of audio includes or isnoise. Alternately, if the maximum amplitude of the portion of the audiosignal does not exceed the moving average of the maximum amplitude(e.g., maximum average plus a small modifier) or the instantaneous voicelikelihood is greater than 0.5 or 50%, it may be determined that theportion of audio is not noise or is speech (e.g., maximum averageexceeded and IVL greater than 50%). Optionally, from operation 410, themethod 400 may return to operation 402 if it is determined that theportion of the audio signal is or includes speech of the participant.

At 412, the limiter ceiling for the audio signal is decreased inresponse to determining that the portion of the audio signal includesnoise. In some aspects, the limiter ceiling is decreased by a specificrate or amount based on an aggregate speech likelihood estimate. Forexample, if the aggregate speech likelihood estimate is high, theceiling limit is decreased by a small amount or slowly toward a minimumlimiter ceiling value. In other cases, when the aggregate speechlikelihood estimate is low, the ceiling limit may be decreased by alarge amount or quickly toward the minimum limiter ceiling value.Alternately or additionally, the minimum limiter ceiling can beconfigured based on the aggregate speech likelihood estimate, an averageof respective amplitudes of multiple portions of the audio signal, or anaverage of respective maximum amplitudes of multiple portions of theaudio signal, such as to represent a portion of current energy estimatedto be speech.

At 414, the limiter ceiling is provided to a limiter module throughwhich the audio signal passes. The limiter module limits, based on thelimiter ceiling, the amount of energy of the audio signal. By limitingthe energy that the audio signal is allowed to transmit or carry into aconferenced audio environment, aspects of adaptive energy limiting mayprevent full energy transient noise from entering the conference audioand disrupting participants and/or other audio-based processes.

Method 500 of FIGS. 5A and 5B is a method performed by a user device 102or a conference device 202. The method 500 scales an audio signal to notexceed a limiter ceiling, which may be effective to prevent the audiosignal from carrying full energy transient noise into a conferencedaudio environment. In some aspects, operations of the method 500 areimplemented by or with an adaptive limiter 110, neural network 112,and/or voice activity detector 114 of the user device 102 or conferencedevice 202.

At 502, a limiter ceiling for an audio signal is set to full scale(e.g., 1.0 or 100%). The limiter ceiling or limiting value may be set tofull scale on initialization of the adaptive energy limiter or reset tofull scale in response to speech by a participant for which an audiosignal is being processed for noise suppression.

At 504, a frame of audio is generated that corresponds to a portion ofthe audio signal. In some cases, the audio signal is received and/orseparated, sliced, or otherwise partitioned into frames of audio foranalysis by the voice activity detector and/or adaptive energy limiter.In other cases, the audio frame may be received from an audio codec orother entity configured to provide frames from the audio signal. Forexample, a frame of the audio may correspond to a range of approximatelyfive milliseconds to 50 milliseconds of audio (e.g., 10 milliseconds).Alternately or additionally, the frame of audio can be converted from atime domain to a frequency domain to enable spectral analysis or otherfrequency domain-based processing.

At 506, the frame of audio is evaluated with a neural network-enabledvoice activity detector to provide an instantaneous voice likelihood(IVL). In some aspects, the portion of the audio signal or a frame ofaudio is evaluated with the neural network or a neural network-enabledvoice activity detector to provide an instantaneous voice likelihood forthe portion of the audio signal or the audio frame. Generally, theinstantaneous voice likelihood may indicate if the audio stream is morelikely speech or more likely noise, which the adaptive energy limiterwould suppress.

At 508, a maximum amplitude of the audio signal is recorded from theframe of audio. The maximum amplitude may be determined or recorded fora duration of audio signal that corresponds to a frame of audio or aduration of audio (e.g., 10 milliseconds). In some cases, the maximumamplitude of the audio signal is compared to a threshold to determine ifa participant is silent, quiet, or otherwise not generating noise. Insuch cases, the method 500 may return to operation 504 if the audiosignal is quiet or silent.

At 510, a moving average of maximum amplitudes for the audio signal isupdated based on the recorded maximum amplitude for the frame of audio.The moving average of maximum amplitudes may correspond to any suitablenumber of audio frames or duration of audio, such as a range ofapproximately 100 milliseconds to 500 milliseconds.

As shown at 512 in FIG. 5B, operation 514 determines an aggregate speechlikelihood estimate (ASLE) based on the instantaneous voice likelihood(IVL) of the frame of audio. The aggregate speech likelihood estimatemay be determined or configured based on a current aggregate speechlikelihood estimate and/or a threshold for detection of voice (ornoise). In some cases, the aggregate speech likelihood estimate isincreased in response to an instantaneous voice likelihood that exceedsthe current aggregate speech likelihood estimate and the threshold forvoice detection. In other cases, the aggregate speech likelihoodestimate is decreased in response to an instantaneous voice likelihoodthat does not exceed the current aggregate speech likelihood estimate orthe threshold for voice detection.

At 516, a determination is made as to whether the maximum amplitudeexceeds the moving average and the instantaneous voice likelihoodindicates the frame of audio is noise. For example, if the maximumamplitude of the portion of the audio signal exceeds the moving averageof the maximum amplitude (e.g., maximum average plus a small modifier)and the instantaneous voice likelihood is less than 0.5 or 50%(indicating noise), the audio frame may include or be noise.Alternately, if the maximum amplitude of the portion of the audio signaldoes not exceed the moving average of the maximum amplitude (e.g.,maximum average plus a small modifier) or the instantaneous voicelikelihood is greater than 0.5 or 50%, the audio frame may not includeor be predominately noise.

Optionally at 518, the limiter ceiling is not decreased in response tothe maximum amplitude not exceeding the moving average and/or theinstantaneous voice likelihood not indicating that the frame of audio isnoise. Optionally at 520, the limiter ceiling is decreased based on theaggregate speech likelihood estimate (ASLE). The limiter ceiling isdecreased in response to the maximum amplitude exceeding the movingaverage and the IVL indicating that the frame of audio is noise.Generally, an amount by which or a rate at which the limiter ceiling isdecreased is determined based on the aggregate speech likelihoodestimate.

At 522, a current value of the limiter ceiling is provided to a limitermodule to scale the audio signal to not exceed the current value. Thelimiter module scales, based on the limiter ceiling, the amount ofenergy of the audio signal that passes through the limiter module. Byscaling or limiting the energy that the audio signal is allowed totransmit or carry into a conferenced audio environment, aspects ofadaptive energy limiting may prevent full energy transient noise fromentering the conference audio and disrupting participants and/or otheraudio-based processes. From operation 522, the method 500 may return tooperation 504 to perform another iteration of the method 500 to furtherlimit energy of the audio signal, reset the limiter ceiling, or maintaina current limiter ceiling. In some aspects, the method 500 or processfor adaptive energy limiting is iterated or repeated approximately everyfive milliseconds to 50 milliseconds (e.g., 10 milliseconds) to provideresponsive suppression of transient noise.

By way of example, consider FIG. 6 in which a graph 600 illustratesaspects of adaptive energy limiting. In the context of a limiter module,energy of an audio signal is passed at full scale 602 or limited to aminimum 604 of the limiter ceiling. In this example, assume the audiosignal 606 is received from a participant that is constantly generatingnoise at a medium to high level (without speech). Here, the adaptiveenergy limiter 110 may quickly limit the energy of the audio signal thatpasses to the conference audio environment to prevent the noise of audiosignal 606 from disrupting other participants of the conference call.

As another example, consider graph 608, which includes an audio signal610 of another participant of the conference call. Here, assume that theparticipant is not speaking, but also not making much noise. Theadaptive energy limiter 110 gradually limits the audio signal 610 untilthe participant begins speaking at 612. In response to detecting speech,the adaptive energy limiter 110 resets the limiter ceiling to full scale602 at 614 and does not begin to limit energy of the audio signal 610until the participant ceases to speak at 616.

Systems

FIG. 7 illustrates various components of an example system 700 that canbe implemented as any type of user device 102 or conference device 202as described with reference to FIGS. 1-6 to implement adaptive energylimiting for transient noise suppression. In some aspects, the system700 is implemented as a component of or embodied on a user equipmentdevice or base station. For example, the system 700 may be implementedas a system of hardware-based components, such as, and withoutlimitation, a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), an application-specificstandard product (ASSP), a system-on-chip (SoC), a system-in-package, acomplex programmable logic device (CPLD), audio codec, audio processor,co-processor, context hub, communication co-processor, sensorco-processor, or the like.

The system 700 includes communication devices 702 that enable wiredand/or wireless communication of system data 704 (e.g., encoded audiodata or audio signals). The system data 704 or other system content caninclude configuration settings of the system, media content stored onthe device, and/or information associated with a user of the device.Media content stored on the system 700 may include any type of audio,video, and/or image data. The system 700 includes one or more datainputs 706 via which any type of data, media content, and/or inputs canbe received, such as human utterances, speech, interactions with a radarfield, user-selectable inputs (explicit or implicit), messages, music,television media content, recorded video content, and any other type ofaudio, video, and/or image data received from any content and/or datasource.

The system 700 also includes communication interfaces 708, which can beimplemented as any one or more of a serial and/or parallel interface, awireless interface, a network interface, a modem, and as any other typeof communication interface. Communication interfaces 708 provide aconnection and/or communication links between the system 700 and acommunication network by which other electronic, computing, andcommunication devices communicate data with the system 700.

The system 700 includes one or more processors 710 (e.g., any ofmicroprocessors, controllers, and the like), which process variouscomputer-executable instructions to control the operation of the system700 and to enable techniques for, or in which can be embodied, adaptiveenergy limiting for transient noise suppression. Alternately oradditionally, the system 700 can be implemented with any one orcombination of hardware, firmware, or fixed logic circuitry that isimplemented in connection with processing and control circuits, whichare generally identified at 712. Although not shown, the system 700 caninclude a system bus or data transfer system that couples the variouscomponents within the device. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures.

The system 700 also includes computer-readable media 714 (CRM 714), suchas one or more memory devices that enable persistent and/ornon-transitory data storage, and thus do not include transitory signalsor carrier waves. Examples of the CRM 714 include random access memory(RAM), non-volatile memory (e.g., any one or more of a read-only memory(ROM), flash memory, EPROM, EEPROM, etc.), or a disk storage device. Adisk storage device may be implemented as a magnetic or an opticalstorage device, such as a hard disk drive, a recordable and/orrewriteable compact disc (CD), any type of a digital versatile disc(DVD), and the like. The system 700 can also include a mass storagemedia device (storage media) 716 or mass storage device interface. Inthis example, the system 700 also includes, or may be implemented as, anaudio codec 722 to support the coding or decoding of audio signals oraudio data, such as to encode audio from a microphone to provide audiosignals or audio data for a conference service or voice call.

The computer-readable media 714 provides data storage mechanisms tostore the device data, as well as various system applications 718 andany other types of information and/or data related to operationalaspects of the system 700. For example, an operating system 720 can bemaintained as a computer application with the computer-readable media714, executed on the processors 710. The system applications 718 mayinclude a system manager, such as any form of a control application,software application, signal-processing and control module, code that isnative to a particular device, an abstraction module or gesture moduleand so on. The system applications 718 also include system componentsand utilities to implement adaptive energy limiting for transient noisesuppression, such as the adaptive limiter 110, neural network 112, andvoice activity detector 114. While not shown, one or more elements ofthe adaptive limiter 110, neural network 112, or voice activity detector114 may be implemented, in whole or in part, through hardware orfirmware.

Although the above-described devices, systems, and methods are describedin the context of adaptive energy limiting for transient noisesuppression in an audio/video conference environment, the describeddevices, systems, or methods are non-limiting and may apply to othercontexts, user equipment deployments, or audio-based communicationenvironments.

Further to the descriptions above, a user may be provided with controlsallowing the user to make an election as to both if and when systems,programs, and/or features described herein may enable collection of userinformation (e.g., audio, sounds, voice, speech, a user's preferences, auser's current location) and if the user is sent content and/orcommunications from a server. In addition, certain data may be treatedin one or more ways before it is stored or used, so that personallyidentifiable information is removed. For example, a user's identity maybe treated so that no personally identifiable information can bedetermined for the user. For example, a user's geographic location maybe generalized where location information is obtained (such as to acity, postal code, or state/province level), so that a particularlocation of a user cannot be determined. Thus, the user may have controlover what information (e.g., audio) is collected about the user, howthat information is used, and what information is provided to the user.

What is claimed is:
 1. A method comprising: setting a ceiling level of alimiter module through which audio signals pass to a full-scale level;receiving, via a data interface, data that comprises a portion of anaudio signal; determining a maximum amplitude of the portion of theaudio signal; evaluating the portion of the audio signal with a neuralnetwork to provide a voice likelihood estimate for the portion of theaudio signal; determining, based on the maximum amplitude and the voicelikelihood estimate, that the portion of the audio signal includesnoise; decreasing the ceiling level of the limiter module from thefull-scale level to a decreased ceiling level in response to determiningthat the portion of the audio signal includes noise; and providing, tothe limiter module through which the audio signals pass, the decreasedceiling level to limit an amount of energy of the audio signal.
 2. Themethod of claim 1, wherein the data is first data, the audio signal is afirst audio signal, the data is received from a first device associatedwith a conferencing service, and the method further comprises: limiting,via the limiter module, the amount of energy of the first audio signalbased on the decreased ceiling level to provide a second audio signal;and transmitting, via the data interface, second data that comprises thesecond audio signal to at least a second device associated with theconferencing service.
 3. The method of claim 1, wherein the portion ofthe audio signal is a frame of audio that corresponds to the portion ofthe audio signal and the method further comprises: prior to evaluatingthe frame of audio, converting the frame of audio from a time domain toa frequency domain.
 4. The method of claim 3, wherein the frame of audiois a first frame of audio and the method further comprises: receiving asecond frame of audio that corresponds to a second portion of the audiosignal; evaluating the second frame of audio with the neural network toprovide a respective voice likelihood estimate for the second frame ofaudio; determining, based on the respective voice likelihood estimate,that the second frame of audio includes speech; and setting the ceilinglevel of the limiter module to the full-scale level.
 5. The method ofclaim 3, wherein the frame of audio is a first frame of audio and themethod further comprises: receiving a second frame of audio thatcorresponds to a second portion of the audio signal; determining arespective maximum amplitude of the second frame of audio; comparing therespective maximum amplitude of the second frame of audio to a thresholdthat corresponds to an average of respective maximum amplitudes ofmultiple frames of audio that correspond to multiple respective portionsof the audio signal; and maintaining a current ceiling level of thelimiter module in response to the respective amplitude of the secondframe of audio not exceeding the threshold.
 6. The method of claim 3,wherein the frame of audio corresponds to a duration of audio thatranges from approximately 10 milliseconds of the audio to approximately50 milliseconds of the audio.
 7. The method of claim 1, whereinevaluating the portion of the audio signal with the neural network toprovide the voice likelihood estimate includes analyzing the portion ofthe audio signal with a neural network-enabled voice activity detector(VAD) to provide an instantaneous voice likelihood (IVL) for the portionof the audio signal.
 8. The method of claim 7, wherein the ceiling levelof the limiter module is decreased by a predefined amount to provide thedecreased ceiling level and the method further comprises: determining anaggregate speech likelihood estimate (ASLE) based on multiple IVLsprovided by the neural network-enabled VAD; updating, based on the IVL,an aggregate speech likelihood estimate (ASLE) by: increasing the ASLEin response to the IVL exceeding the ASLE and exceeding a threshold forvoice detection; or decreasing the ASLE in response to the IVL notexceeding the ASLE or not exceeding the threshold for voice detection;and setting the predefined amount by which the ceiling level of thelimiter module is decreased based on the ASLE.
 9. The method of claim 8,wherein the ceiling level of the limiter module has a minimum value andthe method further comprises: configuring the minimum value of theceiling level based on the ASLE and one of: an average of respectiveamplitudes of multiple portions of the audio signal; or an average ofrespective maximum amplitudes of multiple portions of the audio signal.10. An apparatus comprising: a data interface to receive or transmit anaudio signal over a data network; a limiter module to limit energy ofthe audio signal; a hardware-based processor associated with the datainterface; and storage media storing processor-executable instructionsthat, responsive to execution by the hardware-based processor, implementan adaptive energy limiter configured to: set a ceiling level of thelimiter module for the audio signal to a full-scale level; determine amaximum amplitude for a portion of the audio signal; evaluate theportion of the audio signal with a neural network to provide a voicelikelihood estimate for the portion of the audio signal; determine,based on the maximum amplitude and the voice likelihood estimate, thatthe portion of the audio signal includes noise; decrease the ceilinglevel of the limiter module from the full-scale level to a decreasedceiling level in response to the determination that the portion of theaudio signal includes noise; and provide, to the limiter module, thedecreased ceiling level to reduce the energy of the portion of the audiosignal.
 11. The apparatus of claim 10, wherein: the audio signal is afirst audio signal; the limiter module is configured to limit the amountof energy of the first audio signal based on the decreased ceiling levelto provide a second audio signal; and the storage media storesadditional processor-executable instructions that, responsive toexecution by the hardware-based processor, implement a conferencingservice configured to: receive, the data interface, the first audiosignal from a first device associated with the conferencing service; andtransmit, via the data interface, the second audio signal to at least asecond device associated with the conferencing service.
 12. Theapparatus of claim 10, wherein the adaptive energy limiter is furtherimplemented to: capture, from the audio signal, a frame of audio as theportion of the audio signal; and convert the frame of the audio from atime domain to a frequency domain for evaluation by the neural network.13. The apparatus of claim 12, wherein the frame of audio is a firstframe of audio and the adaptive energy limiter is further implementedto: capture a second frame of audio that corresponds to a second portionof the audio signal; convert the second frame of audio from the timedomain to the frequency domain; evaluate the second frame of audio ofthe audio signal with the neural network to provide a respective voicelikelihood estimate for the second frame of audio; determine, based onthe respective voice likelihood estimate, that the second frame of audioincludes speech; and set the ceiling level of the limiter module to thefull-scale level.
 14. The apparatus of claim 12, wherein the frame ofaudio is a first frame of audio and the adaptive energy limiter isfurther implemented to: capture a second frame of audio that correspondsto a second portion of the audio signal; determine a respective maximumamplitude of the second frame of audio; compare the respective maximumamplitude of the second frame of audio to a threshold that correspondsto an average of respective maximum amplitudes of multiple frames ofaudio that correspond to multiple respective portions of the audiosignal; and maintain the ceiling level of the limiter module at acurrent ceiling level in response to the respective amplitude of thesecond frame of audio not exceeding the threshold.
 15. The apparatus ofclaim 12, wherein the frame of audio corresponds to a duration of audioinformation from the audio signal ranging from approximately fivemilliseconds of the audio information to approximately 50 millisecondsof the audio information.
 16. The apparatus of claim 10, wherein theneural network includes a voice activity detector (VAD) and the adaptiveenergy limiter is further implemented to use the VAD of the neuralnetwork to provide the voice likelihood estimate as an instantaneousvoice likelihood (IVL) for the portion of the audio signal.
 17. Theapparatus of claim 16, wherein the adaptive energy limiter decreases theceiling level of the limiter module by a predefined amount and theadaptive energy limiter is further implemented to: determine anaggregate speech likelihood estimate (ASLE) based on multiple IVLsprovided by the VAD of the neural network; update, based on the IVL, anaggregate speech likelihood estimate (ASLE) by: increasing the ASLE inresponse to the IVL exceeding the ASLE and exceeding a threshold forvoice detection; or decreasing the ASLE in response to the IVL notexceeding the ASLE or not exceeding the threshold for voice detection;and set the predefined amount by which the ceiling level of the limitermodule is decreased based on the ASLE.
 18. A system comprising: ahardware-based processor operably associated with a data interface bywhich data comprising audio signals are communicated; and storage mediastoring processor-executable instructions that, responsive to executionby the hardware-based processor, implement a conferencing serviceconfigured to: set a ceiling level of a limiter module through which theaudio signals pass to a full-scale level; receive an audio signal viathe data interface; generate, based on the audio signal, a frame ofaudio that corresponds to a duration of audio of the audio signal;determine, for the frame of audio, a maximum amplitude of the audiosignal; determine, for the frame of audio, a voice likelihood estimatefor the frame of audio using a neural network; determine, based on themaximum amplitude and the voice likelihood estimate, that the frame ofaudio includes noise; decrease the ceiling level of the limiter modulefrom the full-scale level to a decreased ceiling level in response tothe determination that the frame of audio includes noise; and provide,to the limiter module through which the audio signal passes, thedecreased ceiling level to reduce the energy of the audio signal. 19.The system of claim 18, wherein: the audio signal is a first audiosignal received from a first device associated with the conferencingservice; and the conferencing service is further configured to: limit,via the limiter module, the amount of energy of the first audio signalbased on the decreased ceiling level to provide a second audio signal;and transmit, via the data interface, the second audio signal to atleast a second device associated with the conferencing service.
 20. Thesystem of claim 18, wherein the system is embodied as one of an audioconference system, a cloud-based conferencing service, a videoconference system, an application-specific integrated circuit, anapplication-specific standard product, a system-on-chip, asystem-in-package, a complex programmable logic device, audio codec, oraudio processor.